Abstract
Recent developments in wind speed sensors have mainly focused on reducing the size and moving parts to increase reliability and stability. In this study, the development of a low-cost wind speed and direction measurement system is presented. A heat sink mounted on a self-regulating heater is used as means to interact with the wind changes and a thermopile array mounted atop of the heat sink is used to collect temperature data. The temperature data collected from the thermopile array are used to estimate corresponding wind speed and direction data using an artificial neural network. The multilayer artificial neural network is trained using 96 h data and tested on 72 h data collected in an outdoor setting. The performance of the proposed model is compared with linear regression and support vector machine. The test results verify that the proposed system can estimate wind speed and direction measurements with a high accuracy at different sampling intervals, and the artificial neural network can provide significantly a higher coefficient of determination than two other methods.
Highlights
The popularity and necessity of renewable energy is creating the demand for residential hybrid renewable energy systems (HRES)
We developed a low-cost highaccuracy wind speed and direction sensing system using a thermopile array, a positive temperature coefficient (PTC) heating element and a heat sink
This study reports on the development of a low-cost wind speed and direction sensing system using available and low-cost components, including a thermopile array, a PTC heater, a heat sink, and an ambient temperature sensor
Summary
The popularity and necessity of renewable energy is creating the demand for residential hybrid renewable energy systems (HRES). To efficiently manage energy generated HRESs, real-time monitoring of wind speed is required by many renewable energy related systems. Commercial wind speed sensors are mainly of three types: (a) rotary/cup anemometer, (b) hot-wire anemometer and (c) ultrasonic anemometers. Each of these types has different working principles, their market prices are notably high for residential usage applications. Wind speed data are used for optimal sizing of renewable energy systems [8,9,10,11], energy management systems relying on weather forecasts [12,13,14,15]
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